[mlir][tensor][linalg] Add a pattern that generalizes tensor.pack op.

[hanchung]

Summary

The pattern generalizes a tensor::PackOp into a sequence of tensor +
Linalg ops, when the outer dims are all 1s. It uses the trick of
rank-reduced tensor.extract_slice to get the tile; transpose the tile;
use tensor.insert_slice to insert it to the destination of inner tile.

[tyb0807]

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

532

Nit: typo

[tyb0807]

LGTM

[hanchung]

fix typo, and capture const to lambda

[ftynse]

mlir/test/Dialect/Linalg/generalize-tensor-pack.mlir

64–65

Please do not insert "RUN" lines in the middle of the test file, these are really annoying to find if something goes wrong. Just create a new file instead. Filecheck tests are very cheap.

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

 
 protected:
   OptimizeCopyFn optimizeCopyFn;
   Value createFillOrGenerateOp(PatternRewriter &rewriter, tensor::PadOp padOp,
                                Value dest,
                                const SmallVector<Value> &dynSizes) const;
 };
 
+/// Rewrites a tensor::PackOp into a sequence of tensor.pad + linalg.transpose +
+/// tensor.insert_slice ops, where the tensor::PackOp has outer dims being all
+/// 1s.
+struct GeneralizeOuterUnitDimsPackOpPattern
+    : public OpRewritePattern<tensor::PackOp> {
+  using OpRewritePattern<tensor::PackOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(tensor::PackOp packOp,
+                                PatternRewriter &rewriter) const override;
+};
+
 /// Populates `patterns` with patterns that vectorize tensor.pad.
 /// These patterns are meant to apply in a complementary fashion. Benefits
 /// are used to encode a certain ordering of pattern application. To avoid
 /// scattering magic constants throughout the code base, the patterns must be
 /// added with this function. `baseBenefit` can be used to offset the benefit
 /// of all tensor::PadOp vectorization patterns by a certain value.
 void populatePadOpVectorizationPatterns(RewritePatternSet &patterns,
                                         PatternBenefit baseBenefit = 1);

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/Linalg/Analysis/DependenceAnalysis.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Transforms/HoistPadding.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/Dialect/SCF/Transforms/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tensor/IR/TensorTilingInterfaceImpl.h"
+#include "mlir/Dialect/Tensor/Utils/Utils.h"
+#include "mlir/Dialect/Utils/IndexingUtils.h"
 #include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/Dialect/Utils/StructuredOpsUtils.h"
 #include "mlir/Dialect/Vector/IR/VectorOps.h"
 #include "mlir/IR/AffineExpr.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Support/LLVM.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
Operation *tiledPadOp =
       tensor::bubbleUpPadSlice(rewriter, padOp, sliceOp.getMixedOffsets(),
                                sliceOp.getMixedSizes(), zeroSliceGuard);
   // All shapes are static and the data source is actually used. Rewrite into
   // pad(extract_slice(x)).
   rewriter.replaceOp(sliceOp, tiledPadOp->getResults());
   return success();
 }
 
+/// Returns a tensor.pad op if padding value is set. Otherwise, returns the
+/// source directly. The method assumes that the `packOp` has static shapes.
+static Value getPackOpSourceOrPaddedSource(OpBuilder &builder,
+                                           tensor::PackOp packOp) {
+  Value input = packOp.getSource();
+  if (!packOp.getPaddingValue()) {
+    return input;
+  }
+
+  Location loc = packOp.getLoc();
+  ShapedType inputType = packOp.getSourceType();
+  int64_t inputRank = inputType.getRank();
+  assert(llvm::all_of(packOp.getDestType().getShape().take_front(inputRank),
+                      [](int64_t val) { return val == 1; }));
+
+  SmallVector<int64_t> paddedShape;
+  DenseMap<int64_t, OpFoldResult> tileAndPosMapping =
+      packOp.getDimAndTileMapping();
+  for (int64_t dim = 0; dim < inputRank; ++dim) {
+    int64_t size = inputType.getDimSize(dim);
+    if (!tileAndPosMapping.count(dim)) {
+      paddedShape.push_back(size);
+      continue;
+    }
+
+    // The size is less than or equal to tileSize because outer dims are all 1s.
+    Optional<int64_t> tileSize =
+        getConstantIntValue(tileAndPosMapping.lookup(dim));
+    assert(tileSize.has_value() && "dynamic inner tile size is not supported");
+    paddedShape.push_back(tileSize.value());
+  }
+  auto resultType =
+      RankedTensorType::get(paddedShape, inputType.getElementType());
+  return tensor::createPadHighOp(resultType, input, packOp.getPaddingValue(),
+                                 /*nofold=*/false, loc, builder);
+}
+
+LogicalResult GeneralizeOuterUnitDimsPackOpPattern::matchAndRewrite(
+    tensor::PackOp packOp, PatternRewriter &rewriter) const {
+  // TODO: support the case that outer dimensions are not all 1s A
+  // tensor.expand_shape will be generated in this case.
+  int64_t srcRank = packOp.getSourceRank();
+  if (llvm::any_of(packOp.getDestType().getShape().take_front(srcRank),
+                   [](int64_t val) { return val != 1; })) {
+    return rewriter.notifyMatchFailure(
+        packOp, "require the outer dimension of the result are all 1s");
+  }
+
+  if (llvm::any_of(packOp.getMixedTiles(),
+                   [](OpFoldResult tile) { return tile.is<Value>(); })) {
+    return rewriter.notifyMatchFailure(packOp,
+                                       "require inner tile sizes being static");
+  }
+
+  // 1. Use rank-reduced tensor.extract_slice op to extract the tile.
+  Location loc = packOp.getLoc();
+  Attribute zeroIdxAttr = rewriter.getIndexAttr(0);
+  Attribute oneIdxAttr = rewriter.getIndexAttr(1);
+  SmallVector<OpFoldResult> readOffsets(srcRank, zeroIdxAttr);
+  SmallVector<OpFoldResult> readStrides(srcRank, oneIdxAttr);
+  SmallVector<OpFoldResult> readSizes;
+  SmallVector<int64_t> readShape;
+  DenseMap<int64_t, OpFoldResult> dimAndTileMapping =
+      packOp.getDimAndTileMapping();
+  for (auto i : llvm::seq<unsigned>(0, srcRank)) {
+    if (!dimAndTileMapping.count(i)) {
+      readSizes.push_back(oneIdxAttr);
+      continue;
+    }
+    readSizes.push_back(dimAndTileMapping[i]);
+    readShape.push_back(getConstantIntValue(dimAndTileMapping[i])
+                            .value_or(ShapedType::kDynamic));
+  }
+  Type elemType = packOp.getSourceType().getElementType();
+  auto readType = RankedTensorType::get(readShape, elemType);
+
+  Value input = getPackOpSourceOrPaddedSource(rewriter, packOp);
+  Value tile = rewriter.create<tensor::ExtractSliceOp>(
+      loc, readType, input, readOffsets, readSizes, readStrides);
+
+  // 2. Transpose the tile to match the inner tile order.
+  constexpr int64_t kNonTiledMarker = -1;
+  ArrayRef<int64_t> innerDimsPos = packOp.getInnerDimsPos();
+  SmallVector<int64_t> vec(srcRank, kNonTiledMarker);
+  for (auto [index, value] : llvm::enumerate(innerDimsPos))
+    vec[value] = index;
+  SmallVector<int64_t> perm = llvm::to_vector(llvm::make_filter_range(
+      vec, [&](int64_t v) { return v != kNonTiledMarker; }));
+
+  SmallVector<int64_t> transpShape = readShape;
+  applyPermutationToVector<int64_t>(transpShape, perm);
+
+  Value empty = rewriter.create<tensor::EmptyOp>(loc, transpShape, elemType);
+  auto transposedOp =
+      rewriter.create<linalg::TransposeOp>(loc, tile, empty, perm);
+
+  // 3. Insert the inner tile to the destination.
+  int64_t destRank = packOp.getDestRank();
+  SmallVector<OpFoldResult> writeStrides(destRank, oneIdxAttr);
+  SmallVector<OpFoldResult> writeOffsets(destRank, zeroIdxAttr);
+  SmallVector<OpFoldResult> writeSizes(srcRank, oneIdxAttr);
+  for (auto size : transpShape)
+    writeSizes.push_back(rewriter.getIndexAttr(size));
+
+  auto insert = rewriter.create<tensor::InsertSliceOp>(
+      loc, transposedOp.getResult()[0], packOp.getDest(), writeOffsets,
+      writeSizes, writeStrides);
+  rewriter.replaceOp(packOp, insert.getResult());
+
+  return success();
+}
+
 // The following are patterns for downscaling convolution ops with size-1
 // window dimensions.
 //
 // Note that we'd eventually want to write such transformations in a generic
 // way, e.g., converting to linalg.generic, removing the size-1 dimensions,
 // and then turning back to named ops. But for now it's fine to have a few
 // patterns matching special ops to get started.
 

mlir/test/Dialect/Linalg/generalize-tensor-pack.mlir

+// RUN: mlir-opt -split-input-file --test-linalg-transform-patterns="test-generalize-tensor-pack"  %s | FileCheck %s
+
+func.func @simple_KCRS_to_KCRSsr(%arg0: tensor<1x1x32x8xf32>, %arg1: tensor<1x1x1x1x8x32xf32>) -> tensor<1x1x1x1x8x32xf32> {
+  %0 = tensor.pack %arg0 inner_dims_pos = [3, 2] inner_tiles = [8, 32] into %arg1 : tensor<1x1x32x8xf32> -> tensor<1x1x1x1x8x32xf32>
+  return %0 : tensor<1x1x1x1x8x32xf32>
+}
+// CHECK-LABEL: func.func @simple_KCRS_to_KCRSsr
+// CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK:         %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1]
+// CHECK:         %[[EMPTY:.+]] = tensor.empty() : tensor<8x32xf32>
+// CHECK:         %[[TRANSP:.+]] =  linalg.transpose
+// CHECK-SAME:      ins(%[[TILE]] : tensor<32x8xf32>)
+// CHECK-SAME:      outs(%[[EMPTY]] : tensor<8x32xf32>)
+// CHECK-SAME:      permutation = [1, 0]
+// CHECK:         %[[INSERT:.+]] = tensor.insert_slice %[[TRANSP]] into %[[DEST]]
+// CHECK-SAME:      [0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 8, 32] [1, 1, 1, 1, 1, 1]
+// CHECK:         return %[[INSERT]]
+
+// -----
+
+func.func @simple_pad_and_pack(%input: tensor<5x1xf32>, %output: tensor<1x1x8x2xf32>, %pad: f32) -> tensor<1x1x8x2xf32> {
+  %0 = tensor.pack %input padding_value(%pad : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<5x1xf32> -> tensor<1x1x8x2xf32>
+  return %0 : tensor<1x1x8x2xf32>
+}
+// CHECK-LABEL: func.func @simple_pad_and_pack
+// CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[PAD_VAL:[a-zA-Z0-9]+]]
+// CHECK-DAG:     %[[C0:.+]] = arith.constant 0 : index
+// CHECK-DAG:     %[[C1:.+]] = arith.constant 1 : index
+// CHECK-DAG:     %[[C3:.+]] = arith.constant 3 : index
+// CHECK:         %[[PAD:.+]] = tensor.pad %[[SRC]] low[%[[C0]], %[[C0]]] high[%[[C3]], %[[C1]]]
+// CHECK:           tensor.yield %[[PAD_VAL]]
+// CHECK:         %[[EMPTY:.+]] = tensor.empty() : tensor<8x2xf32>
+// CHECK:         %[[TRANSP:.+]] = linalg.transpose
+// CHECK-SAME:      ins(%[[PAD]] : tensor<8x2xf32>)
+// CHECK-SAME:      outs(%[[EMPTY]] : tensor<8x2xf32>)
+// CHECK-SAME:      permutation = [0, 1]
+// CHECK:         %[[INSERT:.+]] = tensor.insert_slice %[[TRANSP]] into %[[DEST]]
+// CHECK-SAME:      [0, 0, 0, 0] [1, 1, 8, 2] [1, 1, 1, 1]
+// CHECK:         return %[[INSERT]]
+
+// -----
+
+func.func @simple_NC_to_CNnc(%arg0: tensor<32x8xf32>, %arg1: tensor<1x1x32x8xf32>) -> tensor<1x1x32x8xf32>{
+  %0 = tensor.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 8] into %arg1 : tensor<32x8xf32> -> tensor<1x1x32x8xf32>
+  return %0 : tensor<1x1x32x8xf32>
+}
+// CHECK-LABEL: func.func @simple_NC_to_CNnc
+// CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK:         %[[EMPTY:.+]] = tensor.empty() : tensor<32x8xf32>
+// CHECK:         %[[TRANSP:.+]] =  linalg.transpose
+// CHECK-SAME:      ins(%[[SRC]] : tensor<32x8xf32>)
+// CHECK-SAME:      outs(%[[EMPTY]] : tensor<32x8xf32>)
+// CHECK-SAME:      permutation = [0, 1]
+// CHECK:         %[[INSERT:.+]] = tensor.insert_slice %[[TRANSP]] into %[[DEST]]
+// CHECK-SAME:      [0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1]
+// CHECK:         return %[[INSERT]]
+
+// -----
+
+// RUN: mlir-opt -split-input-file --test-transform-dialect-interpreter --canonicalize --test-linalg-transform-patterns="test-generalize-tensor-pack"  %s | FileCheck %s --check-prefix=CHECK-TRANS
+
+func.func @KCRS_to_KCRSsr(%arg0: tensor<1x1x128x64xf32>, %arg1: tensor<1x1x4x8x8x32xf32>) -> tensor<1x1x4x8x8x32xf32> {
+  %0 = tensor.pack %arg0 inner_dims_pos = [3, 2] inner_tiles = [8, 32] into %arg1 : tensor<1x1x128x64xf32> -> tensor<1x1x4x8x8x32xf32>
+  return %0 : tensor<1x1x4x8x8x32xf32>
+}
+// CHECK-TRANS-DAG:   #[[MAP0:.+]] = affine_map<(d0) -> (d0 * 32)>
+// CHECK-TRANS-DAG:   #[[MAP1:.+]] = affine_map<(d0) -> (d0 * -32 + 128, 32)>
+// CHECK-TRANS-DAG:   #[[MAP2:.+]] = affine_map<(d0) -> (d0 * 8)>
+// CHECK-TRANS-DAG:   #[[MAP3:.+]] = affine_map<(d0) -> (d0 * -8 + 64, 8)>
+// CHECK-TRANS:       func.func @KCRS_to_KCRSsr
+// CHECK-TRANS-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-TRANS-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-TRANS:         %{{.+}} = scf.for %[[R:[a-zA-Z0-9]+]] =
+// CHECK-TRANS:           %{{.+}} = scf.for %[[S:[a-zA-Z0-9]+]] =
+// CHECK-TRANS:             %[[IN_R:.+]] = affine.apply #[[MAP0]](%[[R]])
+// CHECK-TRANS:             %[[IN_R_SZ:.+]] = affine.min #[[MAP1]](%[[R]])
+// CHECK-TRANS:             %[[IN_S:.+]] = affine.apply #[[MAP2]](%[[S]])
+// CHECK-TRANS:             %[[IN_S_SZ:.+]] = affine.min #[[MAP3]](%[[S]])
+// CHECK-TRANS:             %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]]
+// CHECK-TRANS-SAME:          [0, 0, %[[IN_R]], %[[IN_S]]] [1, 1, %[[IN_R_SZ]], %[[IN_S_SZ]]] [1, 1, 1, 1]
+// CHECK-TRANS:             %[[TILE:.+]] = tensor.extract_slice %[[SRC_SLICE]]
+// CHECK-TRANS-SAME:          [0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<1x1x?x?xf32> to tensor<32x8xf32>
+// CHECK-TRANS:             %[[EMPTY:.+]] = tensor.empty() : tensor<8x32xf32>
+// CHECK-TRANS:             %[[TRANSP:.+]] =  linalg.transpose
+// CHECK-TRANS-SAME:          ins(%[[TILE]]
+// CHECK-TRANS-SAME:          outs(%[[EMPTY]]
+// CHECK-TRANS-SAME:          permutation = [1, 0]
+// CHECK-TRANS:             %{{.+}} = tensor.insert_slice %[[TRANSP]] into %{{.+}}
+
+transform.sequence failures(propagate) {
+  ^bb0(%arg1: !pdl.operation):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1
+    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 1]
+}
+
+// -----
+
+func.func @pad_and_pack(%arg0: tensor<13x15xf32>, %arg1: tensor<2x8x8x2xf32>, %arg2: f32) -> tensor<2x8x8x2xf32> {
+  %0 = tensor.pack %arg0 padding_value(%arg2 : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %arg1 : tensor<13x15xf32> -> tensor<2x8x8x2xf32>
+  return %0 : tensor<2x8x8x2xf32>
+}
+// CHECK-TRANS:       func.func @pad_and_pack
+// CHECK-TRANS-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-TRANS-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-TRANS-SAME:    %[[PAD_VAL:[a-zA-Z0-9]+]]
+// CHECK-TRANS:         scf.for
+// CHECK-TRANS:           scf.for
+// CHECK-TRANS:             %[[SRC_SLICE]] = tensor.extract_slice %[[SRC]]
+// CHECK-TRANS:             %[[PAD:.+]] = tensor.pad %[[SRC_SLICE]]
+// CHECK-TRANS:               tensor.yield %[[PAD_VAL]]
+// CHECK-TRANS:             } : tensor<?x?xf32> to tensor<8x2xf32>
+// CHECK-TRANS:             %[[EMPTY:.+]] = tensor.empty() : tensor<8x2xf32>
+// CHECK-TRANS:         %[[TRANSP:.+]] = linalg.transpose
+// CHECK-TRANS-SAME:      ins(%[[PAD]] : tensor<8x2xf32>)
+// CHECK-TRANS-SAME:      outs(%[[EMPTY]] : tensor<8x2xf32>)
+// CHECK-TRANS-SAME:      permutation = [0, 1]
+// CHECK-TRANS:         %{{.+}} = tensor.insert_slice %[[TRANSP]] into %{{.+}}
+
+transform.sequence failures(propagate) {
+  ^bb0(%arg1: !pdl.operation):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [1, 1]
+}
+
+// -----
+
+
+func.func @KC_to_CKkc(%arg0: tensor<128x256xf32>, %arg1: tensor<32x4x32x8xf32>) -> tensor<32x4x32x8xf32> {
+  %0 = tensor.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 8] into %arg1 : tensor<128x256xf32> -> tensor<32x4x32x8xf32>
+  return %0 : tensor<32x4x32x8xf32>
+}
+// CHECK-TRANS-DAG:   #[[MAP0:.+]] = affine_map<(d0) -> (d0 * 32)>
+// CHECK-TRANS-DAG:   #[[MAP1:.+]] = affine_map<(d0) -> (d0 * -32 + 128, 32)>
+// CHECK-TRANS-DAG:   #[[MAP2:.+]] = affine_map<(d0) -> (d0 * 8)>
+// CHECK-TRANS-DAG:   #[[MAP3:.+]] = affine_map<(d0) -> (d0 * -8 + 256, 8)>
+// CHECK-TRANS:       func.func @KC_to_CKkc
+// CHECK-TRANS-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-TRANS-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-TRANS:         %{{.+}} = scf.for %[[C:[a-zA-Z0-9]+]] =
+// CHECK-TRANS:           %{{.+}} = scf.for %[[K:[a-zA-Z0-9]+]] =
+// CHECK-TRANS-DAG:         %[[IN_K:.+]] = affine.apply #[[MAP0]](%[[K]])
+// CHECK-TRANS-DAG:         %[[IN_K_SZ:.+]] = affine.min #[[MAP1]](%[[K]])
+// CHECK-TRANS-DAG:         %[[IN_C:.+]] = affine.apply #[[MAP2]](%[[C]])
+// CHECK-TRANS-DAG:         %[[IN_C_SZ:.+]] = affine.min #[[MAP3]](%[[C]])
+// CHECK-TRANS:             %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]]
+// CHECK-TRANS-SAME:          [%[[IN_K]], %[[IN_C]]] [%[[IN_K_SZ]], %[[IN_C_SZ]]] [1, 1]
+// CHECK-TRANS:             %[[TILE:.+]] = tensor.extract_slice %[[SRC_SLICE]]
+// CHECK-TRANS-SAME:          [0, 0] [32, 8] [1, 1] : tensor<?x?xf32> to tensor<32x8xf32>
+// CHECK-TRANS:             %[[EMPTY:.+]] = tensor.empty() : tensor<32x8xf32>
+// CHECK-TRANS:             %[[TRANSP:.+]] =  linalg.transpose
+// CHECK-TRANS-SAME:          ins(%[[TILE]]
+// CHECK-TRANS-SAME:          outs(%[[EMPTY]]
+// CHECK-TRANS-SAME:          permutation = [0, 1]
+// CHECK-TRANS:             %[[SUB_ITER:.+]] = tensor.insert_slice %[[TRANSP]] into %{{[a-zA-Z0-9]+}}
+// CHECK-TRANS-SAME:          [0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<32x8xf32> into tensor<1x1x32x8xf32>
+// CHECK-TRANS:             %{{.+}} = tensor.insert_slice %[[SUB_ITER]] into %{{[a-zA-Z0-9]+}}
+// CHECK-TRANS-SAME:          [%[[C]], %[[K]], 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<1x1x32x8xf32> into tensor<32x4x32x8xf32>
+transform.sequence failures(propagate) {
+  ^bb0(%arg1: !pdl.operation):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [1, 1]
+}

mlir/test/lib/Dialect/Linalg/TestLinalgTransforms.cpp

struct TestLinalgTransforms
   Option<bool> testTransformPadTensor{
       *this, "test-transform-pad-tensor",
       llvm::cl::desc("Test transform pad tensor by copying with generic ops"),
       llvm::cl::init(false)};
   Option<bool> testGeneralizePadTensor{
       *this, "test-generalize-pad-tensor",
       llvm::cl::desc("Test transform pad tensor by copying with generic ops"),
       llvm::cl::init(false)};
+  Option<bool> testGeneralizeTensorPackOp{
+      *this, "test-generalize-tensor-pack",
+      llvm::cl::desc("Test transform that generalize pack ops into a sequence "
+                     "of tensor and Linalg ops"),
+      llvm::cl::init(false)};
   Option<bool> testSwapSubTensorPadTensor{
       *this, "test-swap-subtensor-padtensor",
       llvm::cl::desc("Test rewrite of subtensor(tensor.pad) into "
                      "tensor.pad(subtensor)"),
       llvm::cl::init(false)};
   ListOption<int64_t> peeledLoops{
       *this, "peeled-loops",
       llvm::cl::desc("Loops to be peeled when test-tile-pattern")};

 }
 
 static void applyGeneralizePadTensorPatterns(func::FuncOp funcOp) {
   RewritePatternSet patterns(funcOp.getContext());
   patterns.add<GeneralizePadOpPattern>(funcOp.getContext());
   (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
 }
 
+static void applyGeneralizeTensorPackPatterns(func::FuncOp funcOp) {
+  RewritePatternSet patterns(funcOp.getContext());
+  patterns.add<GeneralizeOuterUnitDimsPackOpPattern>(funcOp.getContext());
+  (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
+}
+
 static void applyExtractSliceOfPadTensorSwapPattern(func::FuncOp funcOp) {
   RewritePatternSet patterns(funcOp.getContext());
   patterns.add<ExtractSliceOfPadTensorSwapPattern>(funcOp.getContext());
   (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
 }
 
 static void applyBubbleUpExtractSliceOpPattern(func::FuncOp funcOp) {
   RewritePatternSet patterns(funcOp.getContext());
void TestLinalgTransforms::runOnOperation() {
   if (testVectorTransferForwardingPatterns)
     return applyVectorTransferForwardingPatterns(getOperation());
   if (testGenericToVectorPattern)
     return applyLinalgToVectorPatterns(getOperation());
   if (testTransformPadTensor)
     return applyPadTensorToGenericPatterns(getOperation());
   if (testGeneralizePadTensor)
     return applyGeneralizePadTensorPatterns(getOperation());
+  if (testGeneralizeTensorPackOp)
+    return applyGeneralizeTensorPackPatterns(getOperation());
   if (testSwapSubTensorPadTensor)
     return applyExtractSliceOfPadTensorSwapPattern(getOperation());
   if (testBubbleUpExtractSliceOpPattern)
     return applyBubbleUpExtractSliceOpPattern(getOperation());
   if (testSwapExtractSliceWithFill)
     return applySwapExtractSliceWithFillPattern(getOperation());
   if (testEraseUnusedOperandsAndResults)
     return applyEraseUnusedOperandsAndResultsPatterns(getOperation());